Empirical dynamic modeling of the association between ambient PM2.5 and under-five mortality across 2851 counties in Mainland China, 1999-2012

Abstract

Background: Ambient fine particulate matter (PM_2.5) pollution has been associated with mortality from various diseases, however, its association with under-five mortality rate (U5MR) has remained largely unknown.

Methods: Based on the U5MR data across 2851 counties in Mainland China from 1999 to 2012, we employed approximate Bayesian latent Gaussian models to assess the association between ambient PM_2.5 and U5MR at the county level for the whole nation and sub-regions. GDP growth rate, normalized difference vegetation index (NDVI), temperature, and night-time light were included as covariates using a smoothing function. We further implemented an empirical dynamic model (EDM) to explore the potential causal relationship between PM_2.5 and U5MR.

Results: We observed a declining trend in U5MR in most counties throughout the study period. Spatial heterogeneity in U5MR was observed. Nationwide analysis suggested that each 10 µg/m³ increase in annual concentration of PM_2.5 was associated with an increase of 1.2 (95% CI: 1.0 - 1.3) per 1000 live births in U5MR. Regional analyses showed that the strongest positive association was located in the Northeastern part of China [1.8 (95% CI: 1.4 - 2.1)]. The EDM showed a significant causal association between PM_2.5 and U5MR, with an embedding dimension of 5 and 7, and nonlinear values θ of 4 and 6, respectively.

Conclusion: China exhibited a downward trend in U5MR from 1999 to 2012, with spatial heterogeneity observed across the country. Our analysis reveals a positive association between PM_2.5 and U5MR, which may support a causal relationship.

Keywords: Ambient air pollution; Empirical dynamic model; Fine particulate matter pollution; Under five mortality.

Fig. 1

Mean U5MR distribution of 2851 counties in China from1999 to 2012.

Fig. 2

The overall spatial trend distribution of U5MR ${(\mathrm{\xi }}_{\mathrm{i}})$ in 2851 counties in China from 1999 to 2012.

Fig. 3

The overall temporal trend of U5MR in 2851 counties, 1999–2012.

Fig. 4

Spatiotemporal distribution of U5MR in 2851 counties over the period 1999–2012.

Fig. 5

Sensitivity analysis on the priors precisions of the spatiotemporal interaction model. Note: The hyperparameters${\mathit{v}}_i,{\vartheta }_i$refer to spatial unstructured residual, and structured residual.${\gamma }_{\mathit{t}}$,${\phi }_t$refer to temporal unstructured and structured hyperparameters.${\delta }_{\mathit{it}}$refers to spatiotemporal interaction hyperparameter.

Fig. 6

Convergence cross mapping (CCM).